EMEIA Sales Finance - Data Scientist
Apple
On-site
London, United Kingdom
Data Scientist
Our team provides data & automation infrastructure to enable commercial insights for EMEIA Sales Finance. We superusers of analytics & BI platforms (e.g. Tableau, SAP BusinessObjects, and various internal tools), databases (e.g. Dremio, Snowflake), and data science platforms (e.g. Dataiku). We help to train others and encourage adoption of these technologies in the wider EMEIA Sales Finance team.
As a Data Scientist, you will be a key driver of our machine learning forecast initiative, supporting the demand forecasting function in Sales Finance. You will be responsible for the end-to-end lifecycle of machine learning models - from ideation and data exploration to deployment and monitoring in a production environment. You will collaborate with cross-functional teams of finance analysts, project managers, and other data scientists to solve some of our most challenging problems and drive AIML adoption across Sales Finance.
- 5 years of hands-on experience building and deploying machine learning models in a production environment.
- Strong proficiency in Python and its core data science libraries (e.g. pandas, scikit-learn, statsmodels, NumPy, PyTorch, TensorFlow, LightGBM)
- Strong proficiency in SQL with hands-on experience querying and manipulating data in modern data platforms like Snowflake or Dremio
- Experience in developing and maintaining data pipelines
- Demonstrable experience with MLOps principles and tools, including workflow orchestration frameworks (e.g. Metaflow).
- Experience in applying machine learning techniques to provide solutions to real business problems, including for time series forecasting
- Solid understanding of the theory behind statistical analysis and machine learning
- Experience with cloud data science platforms: Dataiku (preferred), DataRobot, Databricks, AWS SageMaker, Google Cloud AI Platform, etc.
- Basic experience with deploying infrastructure on cloud platforms like AWS or GCP
- Basic knowledge and understanding of software design principles and how to apply them (SOLID, DRY, modularity, abstraction, consistency, etc.)
- Experience in full data science project delivery lifecycle - from identifying the underlying business needs to delivering projects in a manner that meets those needs
- Curiosity to understand new data science tools and how they can be leveraged to meet business needs
- Ability to translate technical content for non-technical audiences and vice-versa
- Strong verbal / written communication skills
- Creativity to go beyond current tools to deliver the best solution to the problem
- Detail oriented and self-motivated individual able to function effectively when working independently or in a team.
- BS/MS in Data Science/Machine Learning, Mathematics, Statistics, Information Systems, or related field
- Familiarity with MLOps practices is a plus
- Experience with Git is a plus
- Experience using Tableau is a plus
- Experience using BusinessObjects is a plus